{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入工具包\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n",
      "mnist.train.images.shape: (55000, 784)\n",
      "mnist.train.labels.shape: (55000, 10)\n",
      "mnist.validation.images.shape: (5000, 784)\n",
      "mnist.validation.labels.shape: (5000, 10)\n",
      "mnist.test.images.shape: (10000, 784)\n",
      "mnist.test.labels.shape: (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "# 训练集、验证集、测试集的数据形式如下\n",
    "mnist = input_data.read_data_sets('.',one_hot = True)\n",
    "\n",
    "print('mnist.train.images.shape:',mnist.train.images.shape)\n",
    "print('mnist.train.labels.shape:',mnist.train.labels.shape)\n",
    "\n",
    "print('mnist.validation.images.shape:',mnist.validation.images.shape)\n",
    "print('mnist.validation.labels.shape:',mnist.validation.labels.shape)\n",
    "\n",
    "print('mnist.test.images.shape:',mnist.test.images.shape)\n",
    "print('mnist.test.labels.shape:',mnist.test.labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n",
      "[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
      "[[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# 训练集、验证集和测试集前两列的标签如下\n",
    "print(mnist.train.labels[:2])\n",
    "print(mnist.validation.labels[:2])\n",
    "print(mnist.test.labels[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练集上的图片和标签\n",
    "plt.figure(figsize=(16,16))\n",
    "for idx in range(64):\n",
    "    plt.subplot(8,8, idx+1)\n",
    "    plt.axis('off')\n",
    "    plt.title('[{}]'.format(mnist.train.labels[idx]))\n",
    "    plt.imshow(mnist.train.images[idx].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 神经网络的构建\n",
    "# 学习率 lr\n",
    "learning_rate = tf.placeholder(tf.float32)\n",
    "# 输入 x\n",
    "x = tf.placeholder(tf.float32, [None, 784], name='x')\n",
    "# 标签 y\n",
    "y = tf.placeholder(tf.float32,[None, 10], name = 'y')\n",
    "\n",
    "# 构建单层神经网络\n",
    "def initialize(shape, stddev=0.1):\n",
    "    return tf.truncated_normal(shape, stddev=0.1)\n",
    "# truncated_normal从正态分布中截取一部分数据生成指定形状的值\n",
    "L1_units_count = 100\n",
    "W_1 = tf.Variable(initialize([784, L1_units_count]))\n",
    "b_1 = tf.Variable(initialize([L1_units_count]))\n",
    "logits_1 = tf.matmul(x, W_1) + b_1\n",
    "output_1 = tf.nn.relu(logits_1)\n",
    "\n",
    "L2_units_count = 10\n",
    "W_2 = tf.Variable(initialize([L1_units_count, L2_units_count]))\n",
    "b_2 = tf.Variable(initialize([L2_units_count]))\n",
    "logits_2 = tf.matmul(output_1, W_2) + b_2\n",
    "logits = logits_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算logits和y的交叉熵损失。\n",
    "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=logits))\n",
    "train_step = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 反向传播时，采用随机梯度下降法调整权重，优化的目标是交叉熵损失最小\n",
    "pred = tf.nn.softmax(logits)\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(pred, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "grap = tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.framework.ops.Graph at 0x1c44805350>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 300 training steps,the loss is 0.183813, the validation accuracy is 0.9208\n",
      "the test accuracy is: 0.9227\n",
      "after 600 training steps,the loss is 0.231922, the validation accuracy is 0.9468\n",
      "the test accuracy is: 0.9414\n",
      "after 900 training steps,the loss is 0.253143, the validation accuracy is 0.9542\n",
      "the test accuracy is: 0.944\n",
      "after 1200 training steps,the loss is 0.0475333, the validation accuracy is 0.9586\n",
      "the test accuracy is: 0.9546\n",
      "after 1500 training steps,the loss is 0.0201094, the validation accuracy is 0.9596\n",
      "the test accuracy is: 0.9569\n",
      "after 1800 training steps,the loss is 0.336305, the validation accuracy is 0.9614\n",
      "the test accuracy is: 0.9583\n",
      "after 2100 training steps,the loss is 0.217733, the validation accuracy is 0.9586\n",
      "the test accuracy is: 0.9557\n",
      "after 2400 training steps,the loss is 0.126858, the validation accuracy is 0.9666\n",
      "the test accuracy is: 0.9667\n",
      "after 2700 training steps,the loss is 0.0612222, the validation accuracy is 0.9658\n",
      "the test accuracy is: 0.9641\n",
      "after 3000 training steps,the loss is 0.129724, the validation accuracy is 0.968\n",
      "the test accuracy is: 0.9636\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "training_step = 3000\n",
    "lr = 0.6\n",
    "#saver用于保存或恢复训练的模型\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "\n",
    "# 定义验证集与测试集\n",
    "validate_data = {x: mnist.validation.images,y: mnist.validation.labels}\n",
    "test_data = {x: mnist.test.images, y: mnist.test.labels}\n",
    "\n",
    "for step in range(training_step):\n",
    "    batch_x,batch_y = mnist.train.next_batch(batch_size)\n",
    "    _, loss = sess.run(\n",
    "        [train_step, cross_entropy],\n",
    "        feed_dict={\n",
    "            x:batch_x,\n",
    "            y:batch_y,\n",
    "            learning_rate: lr\n",
    "        }) \n",
    "#训练过程中用的是训练集\n",
    "# 每100次训练打印一次损失值与验证准确率\n",
    "    if(step + 1) % 300 == 0:\n",
    "        validate_accuracy = sess.run(accuracy,feed_dict=validate_data)\n",
    "        print('after %d training steps,the loss is %g, the validation accuracy is %g'%(step+1,loss,validate_accuracy))\n",
    "        saver.save(sess,'./model.ckpt',global_step=step)\n",
    "        #最终测试集的准确率\n",
    "        acc = sess.run(accuracy,feed_dict=test_data)\n",
    "        print(\"the test accuracy is:\",acc)\n",
    "#         print('step [{}],entropy loss: [{}]'.format(step+1,loss))\n",
    "#         print(sess.run(accuracy,feed_dict = {x:batch_x,y:batch_y}))\n",
    "#         print(sess.run(accuracy,feed_dict = {x:mnist.validation.images,y:mnist.validation.labels}))\n",
    "#         print(sess.run(accuracy,feed_dict = {x:mnist.test.images,y:mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt-2999\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用训练好的模型做测试\n",
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('.')\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        final_pred, acc = sess.run(\n",
    "            [pred, accuracy],\n",
    "            feed_dict={\n",
    "                x: mnist.test.images[:16],\n",
    "                y: mnist.test.labels[:16]\n",
    "            })\n",
    "        orders = np.argsort(final_pred)\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        print(acc)\n",
    "        for idx in range(16):\n",
    "            order = orders[idx, :][-1]\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(4, 4, idx + 1)\n",
    "            plt.axis('off')\n",
    "            plt.title('{}: [{}]-[{:.1f}%]'.format(mnist.test.labels[idx],order, prob * 100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28, 28)))\n",
    "            plt.show()\n",
    "\n",
    "    else:\n",
    "        pass"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
